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Life-Cycle Inventory of Energy Use and Greenhouse Gas Emissions for Two Hydropower Projects in China

2007· article· en· W2136010411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Infrastructure Systems · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Toronto
FundersCarnegie Mellon University
KeywordsGreenhouse gasHydropowerLife-cycle assessmentRenewable energyLife cycle inventoryEnvironmental scienceSustainabilityElectricityElectricity generationEnvironmental impact assessmentEnvironmental engineeringProduction (economics)Natural resource economicsEngineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Two different sized hydropower projects in China, one with a capacity of 44MW and the other of 3,600MW, were examined through life-cycle assessment (LCA) from the perspective of both sustainability and environmental impact and the influence of project scale. Using the economic input-output based LCA approach, energy use and greenhouse gas (GHG) emissions were quantified. The resulting energy payback ratios were found to be 7 and 48, whereas the normalized GHG emissions were 44 and 6gCO2 equivalent per kW h of electricity production, both in favor of the larger project. Compared with published data on other renewable and nonrenewable options, temperate hydropower, particularly large hydropower, is indicated as an efficient electrical source with relatively low GHG emissions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.233
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it